
import datetime
from IPython.display import Markdown as md
import ccxt
import yfinance as yf
import pandas as pd
import plotly
import plotly.express as px
import statsmodels.api as sm
import scipy.optimize as spop


now = datetime.datetime.now()
md("Last Updated at {}".format(now.strftime("%Y/%m/%d %H:%M")))


exchange = ccxt.kraken ({"enableRateLimit": True})
candles = exchange.fetch_ohlcv("BTC/USD", "1d")
df_btc = pd.DataFrame(candles)
df_btc = df_btc.iloc[:, [0, 4]]
df_btc.columns = ["time", "btc_usd_close"]
df_btc["time"] = pd.to_datetime(df_btc["time"], unit="ms")
df_btc["str_time"] = pd.to_datetime(df_btc["time"], unit="ms").dt.strftime("%Y/%m/%d")


fig = px.line(df_btc, x="time", y="btc_usd_close")
fig.show()


# match start and end date with btc/usd
start_date = list(map(int, df_btc["str_time"].iloc[0].split('/')))
end_date = list(map(int, df_btc["str_time"].iloc[-1].split('/')))
start = datetime.datetime(start_date[0], start_date[1], start_date[2])
end = datetime.datetime(end_date[0], end_date[1], end_date[2])
print(start, end)

# download the stock price 
stock = "MSTR"
df_mstr = yf.download(stock, start=start, end=end, progress=False).reset_index()

2020-05-13 00:00:00 2022-05-02 00:00:00


df_mstr = df_mstr[["Date",  "Close"]].rename(columns = {"Date": "time", "Close": "mstr_close"})
df_mstr["str_time"] = df_mstr["time"].dt.strftime("%Y/%m/%d")


fig = px.line(df_mstr, x="time", y="mstr_close")
fig.show()


df = pd.merge(df_btc, df_mstr, on="str_time", how="inner")

Price chart of BTC/USD¶

Price chart of MSTR¶

Cointegration test¶